In modern driver assistance systems, and also in other contexts, object recognition systems have become an important means, for example, to increase a driver's convenience and safety as well as the safety of the surrounding traffic. Object recognition systems are available which not only detect the presence of an object but which are suited to identify and distinguish between particular objects, or object types, based on recorded image data.
Automatically recognizing a particular object type, for example, based on live images of an onboard car camera, is made difficult by varying imaging conditions in typical use scenarios. Such conditions include the technical properties and settings of the camera(s) used as well as differences in lighting and weather conditions under which an object may be recorded. Moreover, objects of the same object type may appear differently, for example, due to differences in the individual shading or orientation of each object or due to the individual image background.
For an object recognition system to reliably recognize an object in typical use scenarios, therefore, it is often not sufficient to provide a generic model, such as an idealized image representation, of the object or object type. Instead, object recognition systems are known which can be trained by means of machine learning based on a larger number of real-life images of the object or object type. Such images are acquired under a range of variable imaging conditions and are then manually selected and labelled as pertaining to the object type. Based on such labelled, real-life training images the system is then trained to recognize the object type under a corresponding range of possible imaging conditions. Meanwhile, the effect of the described machine learning is typically improved by providing a larger amount of training images recorded under different imaging conditions.
Object recognition systems often need to become adapted to new object types. This occurs, for example, when a driver of a car that is equipped with an object recognition system changes from one environment, for which the system has been trained, to a different environment, for which the system has not been trained. For example, a driver may change his or her position to a region where different traffic-related standards are employed. Such differences often concern the form or the style of traffic signs, road markings, registration plates etc. An object recognition system that is configured to automatically recognize and inform the driver about particular traffic signs, for example, may no longer work reliably in a new environment, as it may fail to recognize traffic signs that correspond to the different standards employed there. In order for a machine learning-based object recognition system to become trained with respect to such new object types, therefore, conventional techniques require the provision of additional sets of training images of each new object or object type. In accordance with the above, this requires that the images of each set have been recorded under a corresponding range of imaging conditions and have been manually labelled as pertaining to the new object type.
Algorithms for image analysis are known which may become computer-implemented or be performed in a computer-assisted way. Moreover, a method for nonlinear component analysis, commonly referred to as Kernel Principal Component Analysis (KPCA), is described in B. Schölkopf et al., “Nonlinear Component Analysis as a Kernel Eigenvalue Problem”, Neural Computation, Massachusetts Institute of Technology, vol. 10, no. 5, 1998, p. 1299-1319.
The provision of recorded and manually labelled training images pertaining to individual object types is time and resource consuming.